7This article explores an automated approach for the efficient placement of substations and 8 the design of an inter-array electrical collection network for an offshore wind farm through 9 the minimization of the cost. To accomplish this, the problem is represented as a number 10 of sub-problems that are solved in series using a combination of heuristic algorithms. The 11 overall problem is first solved by clustering the turbines to generate valid substation positions. 12From this, a navigational mesh pathifinding algorithm based on Delaunay triangulation is 13 applied to identify valid cable paths, which are then used in a mixed-inter linear programming 14 problem to solve for a constrained capacitated minimum spanning tree considering all realistic 15 constraints. The final tree that is produced represents the solution to the inter-array cable 16 results. This method is applied to a planned wind farm to illustrate the suitability of the 17 approach and the resulting layout that is generated.

This article presents a novel framework for the multi-objective optimization of offshore renewable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a floating offshore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the offshore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally efficient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of optimal solutions characterizing how design changes impact the trade-off between these two competing objectives.

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This article explores the application of a wind farm layout evaluation function and layout optimization framework to Middelgrunden wind farm in Denmark. This work applies an evaluation tool to estimate the cost, energy production, and the levelized cost of energy for the existing layout at Middelgrunden wind farm; comparing these against the cost and energy production reported by the wind farm. From here, new layouts have then been designed using both genetic algorithms and particle swarm optimization. This study has found that both algorithms are capable of identifying layouts with reduced levelized cost of energy compared to the existing layout while still considering the specific conditions and constraints experienced by this site. Reductions in levelized cost of energy, such as this can result in very significant savings over the lifetime of the project thereby highlighting the importance of including new advanced methods to wind farm layout design